High-Performance Isotope Labeling for Profiling Carboxylic Acid-Containing Metabolites in Biofluids by Mass Spectrometry
Bibliographic record
Abstract
We have developed a new isotope labeling method, based on the use of isotope-coded p-dimethylaminophenacyl (DmPA) bromide as a reagent, combined with liquid chromatography-mass spectrometry (LC-MS) for high-performance metabolome analysis with a focus on profiling carboxylic acid-containing metabolites. Derivatization is simple, fast (1 h plus 30 min for quenching the reaction), and applicable to a wide range of carboxylic acids with a high yield and little or no side reaction products. This labeling method is demonstrated to be not only effective in introducing an isotope tag for accurate metabolite quantification but also improving the chromatographic retention of the metabolites in reversed-phase (RP) LC, enhancing ESI efficiency by 2-4 orders of magnitude, and facilitating the identification of metabolite peaks in LC-MS. In triplicate experiments of a 1:1 ratio of (13)C-/(12)C-DmPA labeled human urine, we were able to detect 2671, 2546, and 2820 ion pairs from metabolites containing one or more carboxylic acid groups.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".